Microsoft 70-774 - Perform Cloud Data Science with Azure Machine Learning Exam
Page: 3 / 8
Total 37 questions
Question #11 (Topic: )
Note: This question is part of a series of questions that present the same Scenario.
Each question I the series contains a unique solution that might meet the stated
goals. Some question sets might have more than one correct solution while others
might not have correct solution.
You have a non-tabular file that is saved in Azure Blob Storage.
You need to download the file locally, access the data in the file, and then format the data
as a dataset.
Which module should you use?
Each question I the series contains a unique solution that might meet the stated
goals. Some question sets might have more than one correct solution while others
might not have correct solution.
You have a non-tabular file that is saved in Azure Blob Storage.
You need to download the file locally, access the data in the file, and then format the data
as a dataset.
Which module should you use?
A. Execute Python Script
B. Tune Model Hyperparameters
C. Normalize Data
D. Select Columns in Dataset
E. Import Data
F. Edit Metadata
G. Clip Values
H. Clean Missing Data
Answer: E
Question #12 (Topic: )
Note: This question is part of a series of questions that present the same Scenario.
Each question I the series contains a unique solution that might meet the stated
goals. Some question sets might have more than one correct solution while others
might not have correct solution.
Start of repeated Scenario:
A Travel agency named Margies Travel sells airline tickets to customers in the United
States.
Margies Travel wants you to provide insights and predictions on flight delays. The agency
is considering implementing a system that will communicate to its customers as the flight
departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique
airport (the flights Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes
or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD
HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange
and HourlyPrecip.
End of repeated Scenario:
You have an untrained Azure Machine Learning model that you plan to train to predict flight
delays.
You need to assess the variability of the dataset and the reliability of the predictions from
the model.
Which modules should you use?
Each question I the series contains a unique solution that might meet the stated
goals. Some question sets might have more than one correct solution while others
might not have correct solution.
Start of repeated Scenario:
A Travel agency named Margies Travel sells airline tickets to customers in the United
States.
Margies Travel wants you to provide insights and predictions on flight delays. The agency
is considering implementing a system that will communicate to its customers as the flight
departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique
airport (the flights Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes
or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD
HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange
and HourlyPrecip.
End of repeated Scenario:
You have an untrained Azure Machine Learning model that you plan to train to predict flight
delays.
You need to assess the variability of the dataset and the reliability of the predictions from
the model.
Which modules should you use?
A. Cross-validate Model.
B. Evaluate Model.
C. Tune Model Hyperparameters
D. Train Model
E. Score Model
Answer: D
Question #13 (Topic: )
You have an Execute R Script module that has one input from either a Partition and
Sample module or a Web Service input module.
You need to preprocess tweets by using R. The Solution must meet the following
requirements:
* Remove digit
* Remove punctuation
* Convert to lowercase
How should you complete the R code? To answer drag the appropriate value to correct
Target.
[Microsoft-70-774-7.0/Microsoft-70-774-10_2.png]
Sample module or a Web Service input module.
You need to preprocess tweets by using R. The Solution must meet the following
requirements:
* Remove digit
* Remove punctuation
* Convert to lowercase
How should you complete the R code? To answer drag the appropriate value to correct
Target.
[Microsoft-70-774-7.0/Microsoft-70-774-10_2.png]
Answer: [Microsoft-70-774-7.0/Microsoft-70-774-10_3.png]
Question #14 (Topic: )
You are building an Azure Machine Learning Solution for an Online retailer.
When a customer selects a product, you need to recommend products that the customer
might like to purchase at the same time. The recommendation should be based on what
other customers purchased the same product.
Which model should you use?
When a customer selects a product, you need to recommend products that the customer
might like to purchase at the same time. The recommendation should be based on what
other customers purchased the same product.
Which model should you use?
A. Collaborative Filtering
B. Boosted Decision Tree Regression Model
C. Two-Class boosted decision tree
D. K-Means Clustering
Answer: A
Question #15 (Topic: )
You are building an Azure Machine Learning workflow by using Azure Machine Learning
Studio.
You create an Azure notebook that supports the Microsoft Cognitive Toolkit.
You need to ensure that the stochastic gradient descent (SGO) configuration maximizes
the samples per second and supports parallel modeling that is managed by a parameter
server.
Which SGD algorithm should you use?
Studio.
You create an Azure notebook that supports the Microsoft Cognitive Toolkit.
You need to ensure that the stochastic gradient descent (SGO) configuration maximizes
the samples per second and supports parallel modeling that is managed by a parameter
server.
Which SGD algorithm should you use?
A. DataParallelASGD
B. DataParallelSGD
C. ModelAveragingSGD
D. BlockMomentumSGD
Answer: B
